Sampling-based motion planning is one of the fundamental paradigms to generate robot
motions, and a cornerstone of robotics research. This comparative review provides an up-to …

Planning under uncertainty is critical to robotics. The partially observable Markov decision
process (POMDP) is a mathematical framework for such planning problems. POMDPs are …

Self-driving vehicles are a maturing technology with the potential to reshape mobility by
enhancing the safety, accessibility, efficiency, and convenience of automotive transportation …

Many relevant tasks require an agent to reach a certain state, or to manipulate objects into a
desired configuration. For example, we might want a robot to align and assemble a gear …

The artificial potential field approach is an efficient path planning method. However, to deal
with the local-stable-point problem in complex environments, it needs to modify the potential …

One of the fundamental fields of research is motion planning. Mobile manipulators present a
unique set of challenges for the planning algorithms, as they are usually kinematically …

Path planning and trajectory planning are crucial issues in the field of Robotics and, more
generally, in the field of Automation. Indeed, the trend for robots and automatic machines is …

Motion planning is a fundamental research area in robotics. Sampling-based methods offer
an efficient solution for what is otherwise a rather challenging dilemma of path planning …

The open motion planning library (OMPL) is a new library for sampling-based motion
planning, which contains implementations of many state-of-the-art planning algorithms. The …

In this paper, we present CHOMP (covariant Hamiltonian optimization for motion planning),
a method for trajectory optimization invariant to reparametrization. CHOMP uses functional …